Data analysis · Structured data divergence · Quality scoringTraditional equity analysis relies on analyst coverage, management guidance, and sell-side consensus. Our approach inverts this: we begin with comprehensive datasets across structured and unstructured financial sources and let the data surface the thesis. When language shifts materially between consecutive reporting periods, or when structured data diverges from qualitative narratives, our systems flag it as a candidate for deeper analysis.
Every company in our universe receives a continuously updated quality score derived from nine independent signal dimensions. These signals measure financial density, semantic consistency, temporal alignment, source authority, and additional proprietary factors. A high composite score indicates the data corroborates a positive fundamental trajectory across multiple independent dimensions. On the other side of the spectrum, we identify the opposite pattern: signals that individually appear benign but collectively indicate deterioration.
Every thesis is verified against structured financial data before conclusions are drawn. Revenue recognition changes, inventory-to-sales ratios, non-GAAP adjustments, and balance sheet composition are all extracted programmatically and compared against historical baselines. This dual-verification approach — qualitative analysis confirmed by structured financial data — reduces the probability of false signals and increases the expected reliability of each finding.